This example demonstrates the use of Deep Learning APIs to perform Object Detection using both TensorFlow and OpenVINO Optimized Models. The TensorFlow model is converted to OpenVINO model using Model Optimizer. OpenVINO optimizes the TensorFlow model and provides faster inference showed by the OpenVINO Acceleration indicator.
This example uses a pre-trained TensorFlow Object detection SSD_Mobilenet1_Coco model that has been fine tuned using IC defect images. It also shows the optimized model using OpenVINO’s Model Importer. The Optimized Model was optimized using the “convert2ir.py” script that ships with Vision Development Module 2019.
Both models are being loaded and ran using the Model Importer polymorphic API to detect the defects in the IC part presented. This example demonstrates the gain in execution time of the model with National Instrument’s Inference Engine using OpenVINO for the optimization.
For more details, refer to the “OpenVINO Deep Learning in NI Vision” PDF included in the folder.
LabVIEW 2018 64-bit and later
Vision Development Module 2019
Windows 10 64-bit with Intel 6th generation processor or Linux RT 64-bit target
Example code from the Example Code Exchange in the NI Community is licensed with the MIT license.